Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure-aggregation schemes protect privacy against an honest-but-curious server, but existing approaches often suffer from many communication rounds, heavy public-key operations, or difficulty handling client dropouts. Recent methods like One-Shot Private Aggregation (OPA) cut rounds to a single server interaction per FL iteration, yet they impose substantial cryptographic and computational overhead on both server and clients. We propose a new protocol called DisAgg that leverages a small committee of clients called Aggregators to perform the aggregation itself: each client secret-shares its update vector to Aggregators, which locally compute partial sums and return only aggregated shares for server-side reconstruction. This design eliminates local masking and expensive homomorphic encryption, reducing endpoint computation while preserving privacy against a curious server and a limited fraction of colluding clients. By leveraging optimal trade-offs between communication and computation costs, DisAgg processes 100k-dimensional update vectors from 100k 5G clients with a 4.6x speedup compared to OPA, the previous best protocol.

翻译：联邦学习支持分布式客户端协同训练模型，但原始联邦学习会将客户端更新暴露给中央服务器。安全聚合方案能够防止“诚实但好奇”的服务器窃取隐私，但现有方法常受限于通信轮次过多、公钥操作繁重或难以处理客户端掉线等问题。近期如单次私有聚合（OPA）等方案将每轮联邦迭代的交互压缩至单次服务器通信，却给服务器和客户端带来了巨大的密码学与计算开销。我们提出名为DisAgg的新协议，该协议利用称为聚合器的少量客户端委员会来执行聚合：每个客户端将其更新向量秘密共享至聚合器，由聚合器本地计算部分和，仅返回聚合后的份额供服务器端重构。该设计消除了本地掩码和昂贵的同态加密，在降低端点计算量的同时，能防范好奇服务器及有限比例的共谋客户端对隐私的威胁。通过权衡通信与计算成本的最优折中，DisAgg可处理10万个5G客户端的10万维更新向量，相比此前最优的OPA协议实现4.6倍加速。